ermiaazarkhalili/FastContext-4B-RL_base-SFT-Claude-Opus-Reasoning-Unsloth

TEXT GENERATIONConcurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jun 20, 2026License:mitArchitecture:Transformer Open Weights Featherless Exclusive Cold

FastContext-4B-RL_base-SFT-Claude-Opus-Reasoning-Unsloth is a 4 billion parameter language model developed by ermiaazarkhalili, fine-tuned from Microsoft's FastContext-1.0-4B-RL. It is specifically optimized for reasoning distillation using chain-of-thought learning, trained on a dataset of Claude's reasoning traces. This model leverages Unsloth for efficient fine-tuning, offering 2x faster training and 60% less VRAM. It is designed for tasks requiring step-by-step reasoning within a 2,048 token context window.

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Model Overview

This model, developed by ermiaazarkhalili, is a 4 billion parameter language model fine-tuned from Microsoft's FastContext-1.0-4B-RL. It is specifically optimized for reasoning distillation using chain-of-thought (CoT) learning. The fine-tuning process utilized the claude-reasoning-distillation dataset, which comprises 10,477 samples of Claude's reasoning traces, including <think> blocks to facilitate CoT learning.

Key Features & Optimizations

  • Reasoning Distillation: Specialized training on Claude's reasoning traces for enhanced step-by-step problem-solving capabilities.
  • Efficient Fine-tuning: Leverages Unsloth for significant training efficiency, achieving 2x faster training and 60% reduced VRAM consumption.
  • Base Model: Built upon the FastContext-1.0-4B-RL architecture, known for its context handling.
  • Context Length: Fine-tuned with a 2,048 token context window.
  • Quantization: Available in 4-bit QLoRA for efficient inference and GGUF versions for CPU/edge deployment.

Intended Use Cases

  • Complex Reasoning Tasks: Ideal for applications requiring detailed, step-by-step logical deduction.
  • Educational Tools: Can be used to generate explanations or problem-solving steps.
  • Research: Suitable for exploring reasoning capabilities in smaller language models.

Limitations

  • Primarily trained on English data.
  • Knowledge cutoff is limited to the base model's training data.
  • May exhibit hallucinations, generating plausible but incorrect information.
  • Not extensively safety-tuned, requiring external guardrails for sensitive applications.

Finetunes
2 models